How to optimize for inference a simple, saved TensorFlow 1.0.1 graph?

1. You are doing it wrong: input is a graphdef file for the script not the data part of the checkpoint. You need to freeze the model to a .pb file/ or get the prototxt for graph and use the optimize for inference script.

This script takes either a frozen binary GraphDef file (where the weight variables have been converted into constants by the freeze_graph script), or a text GraphDef proto file (the weight variables are stored in a separate checkpoint file), and outputs a new GraphDef with the optimizations applied.

2. Get the graph proto file using write_graph
3. get the frozen model freeze graph

Here is the detailed guide on how to optimize for inference:

The optimize_for_inference module takes a frozen binary GraphDef file as input and outputs the optimized Graph Def file which you can use for inference. And to get the frozen binary GraphDef file you need to use the module freeze_graph which takes a GraphDef proto, a SaverDef proto and a set of variables stored in a checkpoint file. The steps to achieve that is given below:

1. Saving tensorflow graph

 # make and save a simple graph
 G = tf.Graph()
 with G.as_default():
   x = tf.placeholder(dtype=tf.float32, shape=(), name="x")
   a = tf.Variable(5.0, name="a")
   y = tf.add(a, x, name="y")
   saver = tf.train.Saver()

with tf.Session(graph=G) as sess:
   sess.run(tf.global_variables_initializer())
   out = sess.run(fetches=[y], feed_dict={x: 1.0})

  # Save GraphDef
  tf.train.write_graph(sess.graph_def,'.','graph.pb')
  # Save checkpoint
  saver.save(sess=sess, save_path="test_model")

2. Freeze graph

python -m tensorflow.python.tools.freeze_graph --input_graph graph.pb --input_checkpoint test_model --output_graph graph_frozen.pb --output_node_names=y

3. Optimize for inference

python -m tensorflow.python.tools.optimize_for_inference --input graph_frozen.pb --output graph_optimized.pb --input_names=x --output_names=y

4. Using Optimized graph

with tf.gfile.GFile('graph_optimized.pb', 'rb') as f:
   graph_def_optimized = tf.GraphDef()
   graph_def_optimized.ParseFromString(f.read())

G = tf.Graph()

with tf.Session(graph=G) as sess:
    y, = tf.import_graph_def(graph_def_optimized, return_elements=['y:0'])
    print('Operations in Optimized Graph:')
    print([op.name for op in G.get_operations()])
    x = G.get_tensor_by_name('import/x:0')
    out = sess.run(y, feed_dict={x: 1.0})
    print(out)

#Output
#Operations in Optimized Graph:
#['import/x', 'import/a', 'import/y']
#6.0

5. For multiple output names

If there are multiple output nodes, then specify : output_node_names = 'boxes, scores, classes' and import graph by,

 boxes,scores,classes, = tf.import_graph_def(graph_def_optimized, return_elements=['boxes:0', 'scores:0', 'classes:0'])